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Proceedings Year : 2022

Relative Confusion Matrix: Efficient Comparison of Decision Models


Current machine learning and deep learning approaches are cutting-edge methods for solving classification tasks. Comparing the performances of classification models has become a prominent task since the outbreak of these techniques. The performance of such classification models is measured by the ratio between the correctly predicted samples and the others. The most widely used visualization to represent this information is the Confusion matrix. Yet, if this technique is suited to apprehend one model performances, very few works use this representation to compare models. In that paper, we present the Relative Confusion Matrix (RCM), a new matrix visualization that leverages Confusion matrices and a color encoding to expose the class-wise differences of performances between two models. We conduct a user evaluation to compare RCM with two confusion matrix variants. Our results show that RCM encoding leads to a more efficient comparison of two models than existing approaches.
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hal-03706436 , version 1 (27-06-2022)



Luc-Etienne Pommé, Romain Bourqui, Romain Giot, David Auber. Relative Confusion Matrix: Efficient Comparison of Decision Models. IEEE, pp.98-103, ⟨10.1109/IV56949.2022.00025⟩. ⟨hal-03706436⟩


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